28 Sep, 2022

An introduction to AI-driven portfolio construction


In this blog, our summer intern, Luisa Jung provides a high-level introduction on how we incorporate AI into the construction of investment portfolios on our technology platform, AutoCIO, which delivers active, customisable investment strategies at the click of a button.  

Having grown up with technology for as long as I can remember, Artificial Intelligence (AI) has always been an abstract and, admittedly, quite intimidating concept for me. However, my confusion around AI disappeared within my first few weeks at Arabesque. I spoke with the Researchers and Engineers on the AI team and soon had my ‘lightbulb moment’ when I realised that it was the obvious next step for the AI industry to apply itself to financial markets given their incredibly complex and interconnected nature. 

Our web application, AutoCIO, enables clients to create active, hyper-customised, and sustainable investment strategies by choosing from hundreds of user-defined parameters, making it an extremely scalable and cost-effective process towards creating strategies. As you read this blog post, I hope to demystify our use of AI in the strategy construction process and contribute to your understanding of AutoCIO.  

What is the AI Engine?

AutoCIO is powered by our AI Engine, which analyses fundamental behaviours and structures of financial market and evaluates over 25,000 stocks daily with an expectation of expected return. This creates a data foundation that enables the potential output of millions of investment strategies. At its core, the AI Engine consists of an ensemble of machine learning models that analyse a vast amount of financial and non-financial data to distinguish how this information may impact the future return of an asset. (Please read the dedicated blog post from September 2021 to obtain a more detailed explanation.)

Figure 1: AI Engine Input Dataset examples

Portfolio Creation with AutoCIO

Reducing management fees to stay competitive, whilst addressing changing client needs is a major challenge for the asset management industry. To combat the challenges the industry faces, we embrace the application of AI in the portfolio construction process. AutoCIO has the capability of creating traditional, active investment strategies at an enormous scale, making the product agile to the needs of portfolio managers. Millions of potential investment strategies can be simulated in less than two hours for a fraction of the normal research cost. The AI Engine ensures that AutoCIO is continuously adapting to new market trends and behaviours.

Let’s now look at how AutoCIO works: once the AI Engine has analysed the big data input and calculated an expectation of stock return for all the 25,000+ assets, AutoCIO then implements the portfolio construction process. This process consists of three interlinked classes:¹ Rebalance, Optimiser and Backtester.

Figure 2: AutoCIO Strategy Construction 


AutoCIO portfolio construction begins with the Rebalance class, which is essentially the creation of an investible universe – a group of stocks that are considered investible, meaning that they would potentially satisfy a minimal set of conditions or criteria such as liquidity, size etc. How do we create a universe? First, market filters are applied:² Besides selecting the geography (i.e. adding or removing individual countries to be a part of the strategic universe), we can include/exclude certain sectors or industries.

Now that we have created the skeleton of the strategy, we can apply additional filters to further define the universe of stocks. For example, we could filter on liquidity, define the exposure of the strategy by setting the market cap, and/or filter on stock activity. Lastly, we may exclude certain stocks by implementing certain ESG or other style preferences.

By selecting these filters, the included stocks can then be further optimised in subsequent steps. Besides constructing the strategic universe, the rebalance class acts as interface, or a sort of ‘middleman’, between the Optimiser and the Backtester classes, cleaning the data it receives from both sides.


Within the Optimiser class, we decide on the most optimal portfolio within the investible universe at each rebalancing. The objective of the Optimiser class is to choose a portfolio of stocks that maximises a function which considers returns, transaction costs, and an investor’s risk-profile subject to a number of constraints. The first component of the function is the portfolio’s alpha assessment – determined by the AI Engine. The second and third components are portfolio risk and cost appetite, respectively. Risk in AutoCIO is calculated as portfolio variance. For costs, a combination of parameters including trading commission, bid-ask spread, and market impact costs are considered. The coefficients of the equation for risk and cost aversion are negative to reflect that they detract from the alpha component of the portfolio being maximised. The weights for all the stocks in the portfolio (referred to as wi in the equation) correspond with the forecasted alpha, risk and cost components of the function.
Lastly, the Optimiser implements constraints; for example, we may implement a strategy constraint, like long-only (wi ≥ 0), or a risk constraint like maximum position size (wi ≤ max. position size). The magnitude of the coefficients for each of the terms within the function, as well as the accompanying constraints are fully customizable to ensure that each investors objective is being met.

The following equation explains the goal of the Optimiser: 


Once the strategy has been constructed, the Backtester simulates the trades. The Backtester is a historical simulation that replicates how the strategy would have performed historically. AutoCIO can run a wide array of strategies at different frequencies in different regions and markets. Once the backtesting is complete, the strategy is available for clients via the AutoCIO web application. In addition to the client’s customised portfolio strategy, the user interface also offers the possibility to view AutoCIO’s off-the-shelf strategies. The web application allows clients to analyse a strategy using statistics and factor attribution.

Figure 3: Interaction between the classes

Wrapping Up

I hope this blog post has made it clear that AI can be a solution to three key challenges faced by the asset management industry: customisation, cost, and performance. AI enables and facilitates the customisation process by quickly implementing preferences, reducing effort and time. This leads us to the next challenge which AutoCIO solves – cost. Traditional, active investment strategies are normally cost intensive, as they require an ‘army’ of analysts to conduct significant research and due diligence. AutoCIO allows investors to bypass these lengthy and costly processes. Lastly, performance is accounted for by our AI Engine which utilises a vast array of data and the latest advancements in machine learning research to find return opportunities in markets. As our research and the review of our created strategies have shown, strategies created on AutoCIO generally outperform their respective benchmark over 80% of the time and with less volatility.³

AutoCIO provides an opportunity for institutional investors to generate funds at scale with the computational power to analyse high volumes of financial and non-financial data to service a growing demand for customisation and values-based strategies. It’s no longer a case of ‘if’ or ‘when’ AI will enter investment functions. The opportunity for autonomous investing is here now.

Here are some key takeaways about AutoCIO:

  1. Arabesque’s AI Engine produces an expected return assessment for over 25,000 stocks on a daily basis.
  2. Rebalancing allows AutoCIO to consider a large range of preferences and exclusion criteria to construct an eligible universe of stocks.
  3. The Optimiser, based on the AI Engine outputs received, optimises the portfolio from the rebalanced universe to achieve a client’s investment objective.
  4. The Backtester simulates the trades and runs a historic simulation and shows how the stocks would have performed historically.
  5. The use of AI in asset management allows for a more comprehensive analysis of investment strategies and is more agile to consumer/market demands.

¹ In coding, many languages use the concept of objects and classes. Objects are a construct enclosing data and functions. Classes provide the structure for building these objects.

² Instead of selecting from a range of optional filters, it is also possible to select a particular predefined benchmark.

³ 1,200 funds generated by autonomous asset management platform AutoCIO delivered an average of 1.86 percentage points (pp) in excess returns and a 2.89pp reduction in volatility compared with equivalent benchmarks over a ten-year period. 87% of these portfolios outperformed their benchmarks. This effect while stronger in some regions, holds across all regions.

27 Jul, 2022

An Eye Opening Experience: Reflections from the Conference on Computer Vision and Pattern Recognition


This year saw the return of in-person research conferences, and the IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR) was no exception. The event took place in New Orleans, Louisiana from June 19th – 24th (Figure 1). It hosted thousands of researchers from leading academic and industrial institutions (think Google, Meta, Apple, Tesla etc.), and I was fortunate enough to attend the event myself.  
Over the years, computer vision has made an insurmountable contribution to Machine Learning (ML) and Artificial Intelligence (AI). From feature detection [1] (analysing an image and decomposing it into “simpler” features, e.g., edges or shapes), Convolutional Neural Networks 2 (neural networks that rely on correlations, usually measured in spatial dimensions, between “nearby” points in an input by applying convolutional filters across the input space), to autonomous driving [3] (self-driving vehicles). 
With this in mind and considering that CVPR has the highest h-index (a measure of the impact of a conference, based on some function of citation counts) of any AI-related conference, there’s no doubt that the output of this year’s event will move AI forward. In this blog, I highlight what I think are some of the most interesting papers and ideas to come out of this year’s event and their potential implications for AI and ML in general. 

Figure 1: CVPR in the flesh! 

Hyperbolic deep learning 

In [4], hyperbolic deep learning is used to classify what will happen next in a video clip, given the previous clip’s frames.  
The details of how embedding the training of a machine learning model on a hyperbolic geometry are far beyond the scope of this blog post. However, the essence of the problem is that the model attempts to learn a hierarchy of the possible classifications (outputs, that is, what the next video frame will depict) of a problem and uses the curved surface (Figure 2) of a hyperbolic space to represent these hierarchies and to quantify the uncertainty of a classification. Different levels of the hierarchy are meant to represent the certainty of the model’s prediction. 
What this means for AI in general: The idea of training predictive models on a hyperbolic space is completely general to any supervised machine learning task (scenarios where ML is used to generate predictions), and so it will be interesting to see how the concept plays out in other fields. 
On a side note, it’s great to see geometry playing a larger part in AI, akin to how Einstein began to form his theory of General Relativity, based on curved spaces and geodesics. 

Figure 2: Representing prediction uncertainty on a hyperbolic space1. Image taken from the original paper, with permission 

Explainable AI 

The fact that explainable AI received a large amount of coverage at CVPR is somewhat telling: it is an area of growing popularity and importance in CV. 
The gist of the papers is that the output of a classification algorithm is explained in terms of the inputs to the predictive model (usually a CNN). Of course, in most contexts applicable to these papers, the inputs are images, and so the explanations are formed around “groups” of the inputs, pertaining to features detected in the images by the model (e.g. whiskers on a cat). Another common theme in the explainability papers unsurprisingly was the notion of attention [5] which gives some indication of what inputs “attained more attention” to generate the different outputs. Furthermore, the fact that more ready-to-use open-source tools are being made available rather than just being presented as POCs2, is a breath of fresh air, and hopefully takes some of the weight off the few existing easily-deployable packages such as LIME [6] and SHAP [7]. 
One paper in particular that caught my eye was [8]. In this paper, instead of trying to explain a model’s prediction using some approximate method after the prediction has been generated, the network’s mathematical formulation is adjusted a priori so that a by-product of the output of the model provides a direct explanation for the prediction in terms of the model’s inputs.  

Figure 3: Dissecting the informative parts of an input image using [8]. Image taken from the original paper, with permission 

What this means for AI in general: In a field where explanations were not considered pivotal for a long time, it is refreshing to see the topic start to take more of a leading role. Especially as computer vision is applied more and more to real-world scenarios such as medical imaging, and self-driving vehicles, it is crucial that we understand why the blackbox algorithms make the decisions they do. I am confident that this further adoption of explainable AI will continue across different fields in the short-term. 

Domain generalisation 

Another hot topic in CV currently is domain generalisation. It is well-known that deep learning models perform well during live production when the examples considered in this period are drawn from the same data distribution as what the models were trained on. However, when the models are applied to data distributions different compared to what they were trained on, they often don’t perform as well, as the “patterns” they learned during training and don’t persist for the out of distribution data.  
Some interesting papers from CVPR which attempt to address this issue include [9]. In this paper, with the aid of prior knowledge, causal features are learned which are considered to be invariant even across different data distributions. For example, when trying to classify images of different animals, the causal invariant feature would be the profile of the animal, irrespective of the form of the background, which is instead considered as spurious features. 

Figure 4: Causal invariant features (profile of the cow) versus spurious features (image background). Image taken from the original paper, with permission 

 Another paper on this topic from CVPR is [10]. The paper adapts a boosting technique (fitting models to the residuals of the errors of other models’ predictions), applied as an add-on to any deep learning model (Figure 5). In order to improve generalisability, the boosted models are applied to train-cross validation splits of the data during model training, and on a subset of both the most informative and non-informative internal features of the original neural network. During inference, the boosted model used is chosen based on the discrepancy of the input data with the different classes of data used during training. This is measured using a Siamese network (a deep learning model which measures the discrepancy between two sets of inputs). The concept of boosting extends as far back as [11] and is a prominent concept in decision tree-based models. 

Figure 5: Schematic of the BoosterNet add-on for a deep learning model. Image taken from the original paper, with permission 

What this means for AI in general: More focus on domain generalisation is crucial for any scenarios subject to rapidly changing environments, including finance, autonomous driving or weather/natural disaster predicting. 

Tail, few-shot and imbalanced data learning 

Imbalanced datasets (usually pertaining to the target outputs of the model) are a prominent theme in CV. Think, for example, performing facial recognition on an individual, based on a model trained with only one photo of this individual. Another example in the context of image classification is if a classifier has been trained on a particular set of animals to classify cats and dogs, but then needs to be used to classify another breed of animal (e.g. horses). This extreme case of data imbalance is referred to as zero-shot learning [14]. 
On this theme, an interesting paper from CVPR is [15]. In this paper, a method is proposed for optimally selecting pre-trained models to be applied to a new dataset even if the new data contains classes of images not contained in the pre-trained models, or vice versa. Since fine-tuning all potential pre-trained models on the new data is time-consuming, the paper derives a transferability metric (Figure 6)- an indicator of a model’s performance on the new dataset, which can be calculated by passing the new training data through the candidate models just once. The paper shows that this transferability metric correlates well with true performance on the new test datasets. The final selection of models, as chosen by their transferability metric values, are then fine-tuned (trained) on the training data of the new dataset, and final predictions are generated by ensembling the predictions of these models.

Figure 6: Overview of how [15] proposes to select from an array of pre-trained models using a transferability metric, without having to explicitly train these models on new training data. Image taken from the original paper, with permission 

[16] addresses the problem of imbalanced data in a regression (continuous output) setting, (e.g. trying to predict humans’ height). Here the typical loss function used in regression settings (mean squared error) is given a full statistical treatment to derive an adapted loss function which accommodates for any imbalance in data used during model training. The method contains a small additional computational overhead, from the Monte Carlo sampling associated with computing, known as the marginal likelihood/evidence in Bayesian statistics. 
A simple but effective method of dealing with imbalanced datasets is presented in [17]. The authors argue that the weights associated with the last layer of a classification model are imbalanced in norm according to the associated class imbalances (Figure 7). That is, the norm of the weights associated with the components of the probability outputs, in-turn associated with classes which are highly represented in the training data, are much larger than those of the weights associated with underrepresented classes. The authors go on to show that simple regularisation of these weights associated with the output layer of the classification model can counter the class imbalance, and lead to better performance on test sets which are not subject to such imbalanced data. 

Figure 7: Evolution of the norm (colour of the heatmap) of the model weights associated with different classes in the data. For each plot, the rarer a class is, the higher its positioning on the vertical axis. Image taken from the original paper, with permission  

What does this mean for AI in general: Similar to domain generalisation, research into imbalanced data regimes is crucial for any context which is prone to black swan events. 

Final word 

One final note from the conference was the rise of transformers [18] in CV. It was interesting to see that they are now very much considered the state-of-the-art deep learning model when applied to CV [19], and a lot of active research is being put into vision transformers [20, 21, 22]. However, it is good to see that work on CNNs in CV is still going on [23], and the two are even being combined [24]. This makes sense given the original transformer model uses something akin to a convolutional filter during one of its operations. 


[1] – Lindeberg, T., 1998. Feature detection with automatic scale selection. International journal of computer vision, 30(2), pp.79-116. 
[2] – LeCun, Y. and Bengio, Y., 1995. Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks, 3361(10), p.1995. 
[3] – Levinson, J., Askeland, J., Becker, J., Dolson, J., Held, D., Kammel, S., Kolter, J.Z., Langer, D., Pink, O., Pratt, V. and Sokolsky, M., 2011, June. Towards fully autonomous driving: Systems and algorithms. In 2011 IEEE intelligent vehicles symposium (IV) (pp. 163-168). IEEE. 
[4] – Surís, D., Liu, R. and Vondrick, C., 2021. Learning the predictability of the future. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 12607-12617). 
[5] – Bahdanau, D., Cho, K. and Bengio, Y., 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473. 
[6] – Ribeiro, M.T., Singh, S. and Guestrin, C., 2016, August. ” Why should i trust you?” Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1135-1144). 
[7] – Lundberg, S.M. and Lee, S.I., 2017. A unified approach to interpreting model predictions. Advances in neural information processing systems, 30. 
[8] – Böhle, M., Fritz, M. and Schiele, B., 2022. B-cos Networks: Alignment is All We Need for Interpretability. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 10329-10338). 
[9] – Wang, R., Yi, M., Chen, Z. and Zhu, S., 2022. Out-of-distribution Generalization with Causal Invariant Transformations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 375-385). 
[10] – Bayasi, N., Hamarneh, G. and Garbi, R., 2022. BoosterNet: Improving Domain Generalization of Deep Neural Nets Using Culpability-Ranked Features. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 538-548). 
[11] – Bertinetto, L., Valmadre, J., Henriques, J.F., Vedaldi, A. and Torr, P.H., 2016, October. Fully-convolutional siamese networks for object tracking. In European conference on computer vision (pp. 850-865). Springer, Cham. 
[12] – Drucker, H., Cortes, C., Jackel, L.D., LeCun, Y. and Vapnik, V., 1994. Boosting and other ensemble methods. Neural Computation, 6(6), pp.1289-1301. 
[13] – Breiman, L., Friedman, J.H., Olshen, R.A. and Stone, C.J., 2017. Classification and regression trees. Routledge. 
[14] – Socher, R., Ganjoo, M., Manning, C.D. and Ng, A., 2013. Zero-shot learning through cross-modal transfer. Advances in neural information processing systems, 26. 
[15] – Agostinelli, A., Uijlings, J., Mensink, T. and Ferrari, V., 2022. Transferability Metrics for Selecting Source Model Ensembles. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 7936-7946). 
[16] – Ren, J., Zhang, M., Yu, C. and Liu, Z., 2022. Balanced MSE for Imbalanced Visual Regression. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 7926-7935). 
[17] – Alshammari, S., Wang, Y.X., Ramanan, D. and Kong, S., 2022. Long-tailed recognition via weight balancing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 6897-6907). 
[18] – Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł. and Polosukhin, I., 2017. Attention is all you need. Advances in neural information processing systems, 30. 
[19] – Khan, S., Naseer, M., Hayat, M., Zamir, S.W., Khan, F.S. and Shah, M., 2021. Transformers in vision: A survey. ACM Computing Surveys (CSUR). 
[20] – Sun, T., Lu, C., Zhang, T. and Ling, H., 2022. Safe Self-Refinement for Transformer-based Domain Adaptation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 7191-7200). 
[21] – Lin, S., Xie, H., Wang, B., Yu, K., Chang, X., Liang, X. and Wang, G., 2022. Knowledge Distillation via the Target-aware Transformer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 10915-10924). 
[22] – Yang, C., Wang, Y., Zhang, J., Zhang, H., Wei, Z., Lin, Z. and Yuille, A., 2022. Lite vision transformer with enhanced self-attention. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 11998-12008). 
[23] – Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T. and Xie, S., 2022. A convnet for the 2020s. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 11976-11986). 
[24] – Guo, J., Han, K., Wu, H., Tang, Y., Chen, X., Wang, Y. and Xu, C., 2022. Cmt: Convolutional neural networks meet vision transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 12175-12185).

23 Jun, 2022

ESG Book Closes $35 million Series B to Build the World’s Leading ESG Data Platform


New funding will advance ESG Book’s next-generation technology, enabling clients to meet increasingly complex sustainability requirements.

  • Energy Impact Partners led the round alongside Meridiam and Allianz X, as ESG Book responds to growing demand for technology enabled ESG data solutions.
  • The company’s cloud-based platform makes ESG data accessible, consistent, and transparent, enabling financial markets to allocate capital towards more sustainable and higher-impact assets.
  • Investment and global reach of strategic partners will enable ESG Book to expand services worldwide in $5 billion ESG data market.

23rd June, 2022, London and Frankfurt – ESG Book, a global leader in sustainability data and technology, today announced it has closed $35 million in Series B funding. The new capital will be used to advance ESG Book’s next-generation technology capabilities, enabling clients to meet increasingly complex sustainability requirements, and accelerate the company’s expansion as it responds to growing demand for technology enabled ESG data solutions. The round was led by Energy Impact Partners (EIP), a global investment firm leading the transition to a sustainable future, alongside global sustainability leader Meridiam and Allianz X, the digital investments arm of leading global insurer and asset manager Allianz. During the course of the Series B round, shareholders and Series A investors Commerz Real and BMH sold their minority shares in ESG Book, formerly known as Arabesque S-Ray, at an increased valuation versus their initial investments in 2019.

With the ESG data and services market expected to grow to $5 billion globally by 20251, the Series B investment will be used to fuel adoption of ESG Book’s industry-leading data platform, and further the company’s continued expansion into new products areas.

The company’s cloud-based platform makes ESG data accessible, consistent, and transparent, enabling financial markets to allocate capital towards more sustainable and higher-impact assets. Covering over 25,000 companies globally, ESG Book enables companies to be custodians of their own data, provides framework-neutral sustainability information in real-time, and promotes transparency.

Dr Daniel Klier, CEO of ESG Book, said: “Investors, companies, and all market participants are today demanding better, technology-enabled solutions in order to direct capital towards more sustainable and higher-impact assets. ESG Book is disrupting how sustainability is integrated and measured on a global scale by using next-generation technology that makes ESG data accessible, comparable and transparent. By partnering with three of the world’s leading sustainability conscious investors, EIP, Meridiam, and Allianz X, we are excited about the next chapter of our company’s growth as we scale ESG Book’s platform and services worldwide.”

Nazo Moosa, Managing Partner, Europe, at Energy Impact Partners, said: “We are delighted to welcome ESG Book to our family of companies that empower the transition to net zero and support the principles of sustainability. ESG Book marks the tenth investment by Energy Impact Partners in Europe, and this partnership is driven by a shared vision for radical transparency in sustainability data. We look forward to supporting the tremendous momentum of the company as it builds the world’s leading ESG data platform.”

Thierry Deau, Founder and CEO of Meridiam, said: “ESG Book is a platform with the potential to transform the way ESG data is processed by the financial world. We believe it will substantially increase the quality and availability of ESG information to direct financing flows in accordance with sustainable development goals and the Paris Agreement. As impact investors since inception, Meridiam has been confronted with the lack of data transparency and has developed strong expertise in impact measurement. Through this investment by the Green Impact Growth Fund, we will further contribute to the field by helping ESG Book become the reference player in the sustainability data field.”

Carsten Middendorf, Head of Platforms & Acquisitions at Allianz X, said: “As an investor, we know how important data is for making decisions. Sustainability isn’t just a fad or a phase. It’s our necessary present if we are to have a future. That’s why it’s so important to ensure transparency, quality, and comparability in ESG data. We at Allianz X invest in the future, which is why we’re supporters of ESG Book.”

The state of Hesse invested in ESG Book, formerly known as Arabesque S-Ray, through BM H in 2019. With the successful closure of the Series B round led by new private investment, the state of Hesse has now sold the BM H holdings, fulfilling its role in supporting the company’s early growth.

16 Jun, 2022

Ortec Finance Announces Partnership with ESG Book


Ortec Finance’s collaboration with ESG Book will provide clients with a one-stop shop for sustainability analysis.  

  • ESG Book, formerly Arabesque S-Ray, combines cutting-edge technology and research to make sustainability data more widely accessible across financial markets.  
  • Investors will gain access to ESG Book’s suite of metrics and data on corporate emissions, green revenues, and regulatory solutions.  
  • Launching later in 2022, Ortec Finance is developing an on-demand Implied Temperature Rise (ITR) analytics platform for Climate ALIGN, powered by ESG Book’s market-leading climate data. The platform will allow clients to access ITR scores across multiple asset classes including public equities, credit, private markets and sovereign debt.  

16 June 2022, London, Rotterdam – ESG Book, a global leader in sustainability data and technology, and Ortec Finance, the leading provider of technology and solutions for risk and return management, today announced a new partnership to deliver next-generation ESG data and insights to investors.    

Through the collaboration, Ortec Finance will leverage ESG Book’s suite of climate and sustainability data solutions to provide clients with a one-stop shop for sustainability analysis.  

Ortec Finance’s partnership with ESG Book, formerly Arabesque S-Ray, will enable investors to access a wide range of metrics and data on corporate greenhouse gas (GHG) emissions and green revenues, as well as regulatory solutions addressing Sustainable Finance Disclosure Regulation (SFDR), Task Force on Climate-Related Financial Disclosures (TCFD) and EU Taxonomy requirements. 

Launching later in 2022, Ortec Finance is developing an on-demand Implied Temperature Score (ITR) analytics platform for Climate ALIGN, which will be powered by ESG Book’s market-leading climate data. The platform will allow clients to access ITR scores across multiple asset classes including public equities, credit, and private markets. ITR scores will be available for individual securities, and at an aggregate level across asset classes and portfolios.   

Ton van Welie, CEO of Ortec Finance, said: “We are thrilled about the partnership between ESG Book and Ortec Finance. The financial impact of climate risk, combined with net zero alignment, are increasingly taking centre stage in investment decision-making. By combining ESG Book’s sustainability data and technology with the models and methodologies of Ortec Finance, we are able to provide our clients with the analytics and insights that enable them to manage the increasing complexity of investment decision-making.”  

Dr Daniel Klier, CEO of ESG Book, said: “With a shared vision to empower investment decisions through transparency, we are delighted to be collaborating with Ortec Finance to accelerate client access to comparable, reliable ESG data and insights. With the growing focus on the transition to a net zero economy, investors worldwide are developing more robust approaches to climate scenario analysis, and through this partnership, we are excited to deliver the best solutions in the market”. 

16 May, 2022

Sustainable Finance Regulatory Update: April 2022 


This month, climate continues to be the primary focus of mandatory disclosures as the UK and Canada adopt TCFD-aligned reporting requirements. The European Commission and World Federation of Advertisers articulate the dangers of unsubstantiated green claims in product marketing and introduce measures to prevent greenwashing. South Africa, Colombia and Sri Lanka outline taxonomies in line with international standards with an overlay of local environmental and social priorities. India ramps up its ESG compliance standards for top companies. UK’s Financial Conduct Authority evaluates company performance in terms of board diversity as it sets ‘comply or explain’ standards. Standard-setting body ISSB furthers cooperation with representatives from different jurisdictions to improve scalability of its global baseline standards. To learn more about the most recent updates, read on.  


EU Commission adopts technical standards for sustainability-related financial disclosures 

The European Commission clarified disclosure rules for financial market participants under the Sustainable Finance Disclosures Regulation (SFDR). The adopted provision will apply from 1 January 2023. In response to emerging interpretations of SFDR guidance, the EC will require participants to provide information in a specified manner. Under the new rule, financial market participants must disclose the negative economic and social impacts of investments which will help determine the sustainability performance of financial products. Data quality improvements and harmonization could be starting points in promoting coherence and preventing greenwashing. Read more

EU Proposed on new Ecodesign Requirements for Sustainable Products

The Proposal for a Regulation establishing Ecodesign requirements for sustainable products (ESPR) was released last week. The recently released ESPR will extend the scope of the Ecodesign Directive to non-energy products except for food and medicine. The regulation will provide a general framework enforcing ecodesign requirements for products sold in the EU market. The Proposal is part of the European Sustainability Initiative (ESI), a package of inter-related monitoring protocols that includes communication on product sustainability, textiles and a proposed consumer rights empowerment directive. Existing ESI directives will be in dialogue with future directives regulating the advertisement of products with green claims. Read more

United Kingdom

UK enforces TCFD-aligned reporting requirements 

The UK is set to impose climate-related reporting requirements in line with TCFD recommendations from 6 April 2022. The new law will require 1300 of UK’s largest registered companies and financial institutions to report on climate-related risks and opportunities, a major step towards achieving the government’s goal of becoming the world’s greenest financial system. The UK aims to improve data quality and availability for high impact businesses through climate-related reporting ahead of COP26 and G20 summits. As the UK reinforces its commitment to net zero by 2050, it will endorse due diligence as the key operational principle, encouraging companies to internalize processes of climate-related risk assessment. Read more

FCA to require UK-listed companies to disclose on board diversity

The Financial Conduct Authority (FCA) will require listed companies to disclose on board and executive diversity targets from financial period starting 1 April 2022. The ‘comply or explain’ statement targets require 40% women on the board, at least one woman in a senior board position (C-suite executive or Senior Independent Director) and one board member from a non-White ethnic minority background. FCA’s analysis of company diversity will also extend to key board committees, including audit and remuneration committees. Read more

UK launches taskforce to develop ‘gold standard’ for UK companies’ climate transition plans

HM Treasury launched a taskforce to develop a ‘gold standard’ for UK companies’ climate transition plans. In the UK, as of 2023 large companies and certain financial sector firms will be required to publish a transition plan. Climate transition plans are an important tool to provide insights on how to reach net zero by 2050 by setting intermediate milestones. It is important to develop a credible and reliable framework on how these transition plans should like and the requirements to be met. Read more


Canada to impose mandatory climate disclosures on banks and insurers

Canada’s budget added a special provision that will require banks and insurers to provide climate disclosures in line with the TCFD framework. Under the new initiative, the Office of the Superintendent of Financial Institutions (OSFI) will hold consultations with multiple stakeholders in the financial system to assess the proposed implementation of climate-related disclosures starting in 2024. The OSFI would require financial institutions to collect emissions data and climate risk information from clients. Additionally, the government will incorporate ESG disclosure requirements for federally regulated pension plans. Read more

Colombia publishes LatAm’s first Green Taxonomy 

Colombia leads the way in Latin American sustainable finance by launching its own Green Taxonomy. In essence, the classification system replicates EU’s approach for determining the contribution of projects, activities and assets to key environmental objectives. However, Colombia’s Taxonomy differentiates itself by underscoring the importance of land use in sustainability reporting. The country has taken a huge step towards reinforcing its climate goals by focusing on the regulation of priority sectors, specifically, agriculture, forestry and livestock. 59% of Colombia’s greenhouse gas emissions can be attributed to the agriculture, forestry and livestock sector, therefore it is likely that these sector activities will be subject to additional screening criteria and minimum threshold requirements in the future. Read more 


South Africa launches first Green Finance Taxonomy edition

South Africa’s Treasury released the first edition of its Green Finance Taxonomy with the goal of creating locally adapted rules and sustainability standards for market participants. The Taxonomy is a ‘living document’ that seeks to drive large-scale climate-friendly capital allocation decisions and green investments through increased transparency. The ‘green’ design of South Africa’s recent environmental provision remains embedded in customary contours of global governance as it is modeled after the EU Taxonomy. Defining the social cost of mitigation activities is tough in emerging economies. South Africa may need to address the entanglement of trade and environmental issues. Additionally, the country must contribute analysis of sustainability standards to inform inclusive monitoring protocols and approaches. Read more


India’s Securities and Exchange Board adopts ESG reporting rules

India’s financial regulatory authority will require the country’s top 1000 companies by market cap size to disclose along with annual stock exchange filings a Business Responsibility and Sustainability Report (BRSR). The new rules on good governance will establish best practices for evaluating enterprise value in line with a company’s overall ESG impact.  So far, the Indian government has limited its role of monitoring compliance with an overarching sustainability agenda and businesses have taken initiative to introduce their own sustainability aligned KPIs. The new measure underscores the need for harmonization and institutional oversight of business practices. Read more

Sri Lanka’s Green Taxonomy is under development

Sri Lanka’s central bank is currently developing a green finance taxonomy for the banking sector that will help provide a framework for the assessment of enterprise value and guide green investments. The country plans to reduce its reliance on petroleum products and shift to production and consumption of renewable sources of energy. The transition to low carbon energy sources will not only reduce Sri Lanka’s expenditure on imported fossil fuels but also reduce its greenhouse emissions. By creating parallel public and private incentives to comply with climate standards, Sri Lanka hopes to establish a robust sustainable energy infrastructure. Read more  


ISSB establishes working group to enhance compatibility between global baseline and jurisdictional initiatives

Leading standard-setter ISSB has established a working group to create an ongoing dialogue between jurisdictional initiatives on sustainability reporting and ISSB’s exposure drafts. ISSB will solicit feedback from multiple stakeholder groups as this will inform the development of a scalable global baseline. These standards, which will have an initial focus on climate requirements, can be adopted on a voluntary basis by market participants or be embedded in public policy. Read more

World Federation of Advertisers issues guidance on making credible environmental claims

Amid growing concerns about greenwashing the World Federation of Advertisers issued guidance to strengthen self-assessment for products making environmental claims. The guidance requires marketing and communications materials to be accompanied with ‘robust evidence for all claims likely to be regarded as objective and capable of substantiation’. WFA intends to create an overarching set of sustainability-linked marketing principles that allow consumers to make informed decisions when comparing products. Read more

Other News & Resources 

  • The World Benchmarking Alliance has published the methodology for its forthcoming benchmark that will rank 1,000 companies on their nature-related impacts and dependencies. Each company will receive a score based on 25 nature indicators and 18 social factors when the benchmark is released in December. 
  • The International Monetary Fund has created a loan-based Resilience and Sustainability Trust to help countries build resilience to external shocks and ensure sustainable growth. Complementing the IMF’s existing lending toolkit, this will focus on longer-term structural challenges, including climate change and pandemic preparedness. About three-quarters of IMF member states will be eligible. 
  • During the City Week 2022 conference in London, International Sustainability Standards Board chair Emmanuel Faber said the organisation will soon launch a platform for jurisdictions working on climate disclosures with the aim of aligning efforts. 
  • Science Based Targets initiative launches net-zero finance standard development process with Foundations paper. Read more


  • NGFS consultation on its repository of climate data needs and available sources: This public consultation seeks feedback on the directory web interface through a short online questionnaire. The consultation is open until May 6, 2022, COB. Read more
  • SEC Climate rule: On March 21, 2022, in a landmark proposal, the US Securities and Exchange Commission (“SEC”) proposed rules that would require public companies to disclose extensive climate-related information in their SEC filings. The proposal is open for public comment through at least May 21, 2022Read more
  • EC Targeted consultation on the functioning of the ESG ratings market in the European Union and on the consideration of ESG factors in credit ratings: 4 April 2022 – 6 June 2022. Link
  • ISSB Exposure Drafts: International Sustainability Standards Board has started a public consultation on climate-related financial disclosures. The ISSB seeks feedback on the proposals until 29 July 2022. Read more
  • EBA launches discussion on the role of environmental risks in the prudential framework: The European Banking Authority (EBA) published a Discussion Paper on the role of environmental risks in the prudential framework for credit institutions and investment firms. The Paper explores whether and how environmental risks are to be incorporated into the Pillar 1 prudential framework. The consultation runs until 2 August 2022. Read more
  • EFRAG Consultation on the exposure drafts: EFRAG’s Due Process establishes public consultations as a key step of its standard setting activities. EFRAG therefore wishes to announce that it will launch a public consultation on the first set of exposure drafts around the end of April. The consultation deadline will be 8th August 2022. Read more


27 Apr, 2022

Quant’s Place in the ESG World: Engagement at Quant Funds


We are in the middle of annual general meetings season, where companies present their annual reports and shareholders get to vote on key matters. Financial institutions have found themselves at the forefront of sustainability discussions, engaging with corporates and influencing their decision-making on these matters. With good governance, environmental and social topics becoming increasingly prominent around the world, it is in an investor’s interest to oversee how investee companies manage sustainability risks for the benefit of their portfolios and for the benefit of the planet. Engagement with companies is the most effective way to convey change- treating sustainability as a real-world issue rather than merely a portfolio optimisation problem.


The challenge with systematic strategies is that the rules guiding investment decisions are vested in the investment model, and PMs are not taking discretionary decisions. As such, you cannot often apply the traditional engagement tools available to investors, i.e.:

  • Filing a shareholder resolution to escalate matters
  • Vote against management of a company during annual general meetings
  • Divestment, if an engagement with the corporate fails

The reason for this challenge is that the equity ownership required to pursue the above-mentioned approaches means you need to be invested in the company. As a quantitative asset manager, you simply don’t know if you will be invested in the company at the time of their annual general meeting. The market conditions can change, and the company can be dropped from the portfolio as part of the model recalculation. Not to mention the turnover, which is precisely the reason why quant houses are sometimes discouraged from getting involved with engagement initiatives. 

How did we approach this?

Arabesque is built on two pillars: sustainability and AI. These two pillars form the foundation of our investment philosophy and are reflected in the investment process of all our strategies. Our shareholders place their trust with Arabesque to manage their money in line with these principles: by directing capital towards companies that are fit for future, while utilising the latest AI technologies.

As a company with sustainability at the core of our mission and as manager with fiduciary duties, we believe that quantitative houses do have a role to play. We currently utilise the following approaches to integrate stewardship considerations:

  1. Proxy voting: Voting is an obvious place to start but is often dismissed as a tick-boxing exercise. However, not all votes get cast as many can get lost in passive products – leading to the loss of shareholders having a say in company’s decisions. The power of proxy battles should not be underestimated. For example, in 2021, investors voted out 3 of the 12 Exxon Mobile Board Directors due to their unsuitable experience to lead the oil giant in the transition to a low-carbon economy. Therefore, we need the largest shareholders to act and smaller shareholders to voice their concern too. The fund behind the Exxon engagement campaign held only 0.02% of the company but was able to create significant changes at the top management. At Arabesque, we cast votes for companies in all our funds and in line with ESG Voting Policy. Our sustainability team monitors the votes on daily basis and consults the Arabesque Sustainability Committee.
  2. Collaborative engagement: Investor engagement campaigns are likely to be more effective when supported by larger number of financial institutions. Collaborative initiatives provide space for idea sharing, combined analysis and fosters an environment of financial institutions working towards the same goal. The obvious benefit of collaboration is the leverage you gather by combining the AUM of various asset managers and owners. Arabesque is currently part of various collaborative initiatives via ClimateAction100+, Share Action and the Investor Decarbonisation Initiative. We often don’t hold positions in the companies they engage with, but we leverage our brand and provide data for further analysis.
  3. Our engagement campaign: Arabesque launched an engagement campaign focusing on the improvement of GHG emission disclosure in the European technology sector. With net-zero commitments being announced every week, we need data to assess them as what cannot be measured, cannot be managed. Given our expertise in sustainability data, we believe that this is an area, where we can play our part in the world of investor engagement initiatives. Data availability and quality is a key concern to effective performance of the investment strategy and in turn a key concern to our shareholders who trust us with their money. The campaign was supported by investors with $970bn in AUM.
    Further details and methodology behind our campaign will be explored in our next newsletter – stay tuned).

The verdict: do quants have a role to play?

The answer is absolutely. We are aware, that out efforts are only part of a larger puzzle of actions required for the global just transition to low-carbon economy. But the journey is long and complex, so we need all hands-on deck to get going, including the ones of quant managers.

Will Arabesque be able to move the needle? Probably not- but we cannot afford to wait as the risks of inaction is greater.

1 Apr, 2022

Sustainable Finance Regulatory Update: March 2022


With a growing influence and reach of non-state actors, there has consistently been an effort to design standards and frameworks aligning the activities of the private sector with sustainability objectives. In the context of trade, the legitimacy of EU’s climate sovereignty will be preserved using a new carbon-leakage mitigation measure. The S.E.C. has sounded off the alarm bells for climate-related financial risk, but progressive rulemaking to this effect is tricky in Washington. A new biodiversity successor to TCFD – the TNFD – brings the topic of biodiversity into focus. Leading standard-setters GRI and IFRS will work together to develop a sustainability reporting ‘baseline’ through an investor-focused lens. These are some of the major ESG regulatory updated featured in our March 2022 update below. 

EU imposes carbon tax on imported products 

EU countries have reached an agreement on a Carbon Border Adjustment Mechanism (CBAM) to prevent the risk of carbon leakage. The policy will take aim at companies outsourcing carbon emissions abroad, where it may be easier to eschew manufacturing processes oversight. CBAM will be implemented in 2023 to address the offsetting of EU’s GHG emissions reduction efforts and lighten transition risk for companies importing carbon-intensive products to high-value markets. Read more  

EU Taxonomy technical criteria: non-climate objectives and environmental transition taxonomy report published 

The EU boosts its commitment to accountability criteria with its latest publication of technical recommendations for the disclosure of the remaining four non-climate environmental objectives – marine conservation, pollution prevention, circular economy, biodiversity and ecosystems. The new Platform’s report covers more than 60 economic activities in 12 sectors, including manufacturing, transport, agriculture, fishing, buildings and disaster risk management. The European Commission is expected to draft a new Delegated Act building on the Platform’s recommendations in the autumn. Read more 

SEC proposed climate rule

 On 23 March 2022, the SEC published a proposed rule that would require public companies to disclose climate change related risks that “are reasonably likely to have a material impact on its business, operating results in financial condition. The rule, once finalized, will require Scope 1 and 2 GHG emissions metrics to be disclosed separately, shown both by disaggregated constituent GHGs and in the aggregate, as well as in absolute and intensity terms. The SEC offers leeway for supply chain emissions, as Scope 3 emissions reporting is subject to materiality. Read more 

TNFD releases beta version of framework for nature-related risk management 

TNFD released the first version of a framework to demystify the concepts and definitions of nature-related risks and opportunities. It follows an “open innovation” approach by soliciting feedback from market participants on an online platform. The framework’s beta version offers guidance to support internal strategy and risk management processes within corporations and financial institutions, thus illustrating the function of disclosure findings to inform corporate and capital allocation decisions. Read more  

GRI and IFRS announce collaboration on global sustainability standards 

The Global Reporting Initiative (GRI) and the IFRS Foundation have agreed to jointly develop a global baseline for investor-focused sustainability reporting standards. GRI’s sustainability reporting requirements will complement IFRS’s investor-focused capital markets standards to promote good governance schemes and meet multi-stakeholder needs. Both organizations’ standard-setting boards will work in a consultative capacity in the lead up to ISSB’s draft publication of “game changing” standards for climate reporting published on 31st March 2022 week. Read more

Republic of Korea enacts Carbon Neutrality and Green Growth Act for Climate Change 

South Korea enacted the Carbon Neutrality and Green Growth Act for the Climate Change. In furtherance of targets set forth at COP26, Korea will require its government to cut its greenhouse gas emissions by 35% of 2018 levels and achieve carbon neutrality by 2050. The law entered into force on 25th March 2022. Read more 

Other News & Resources 

  • BIS Working Paper: Deconstructing ESG scores: In this working paper, BIS proposes sustainability impact assessment by breaking down ESG scores into granular components. In what is called a “targeted” ESG investment strategy, asset managers may exclude firms with the lowest selected ESG category scores and reinvest proceeds in firms with the highest scores having the same regional and sectoral composition. Read more 
  • U.S. Department of Labor takes measures to protect retirement savings from climate-related risks: The U.S. Department of Labor has issued a Request for Information seeking input from the public on agency actions to protect retirement savings from climate-related risks. Read more 
  • Bursa Malaysia doubles down on sustainability reporting: Malaysia’s Stock Exchange has proposed amendments to the “Listing Requirements” in a public consultation paper in order to enhance sustainability reporting practices and climate-related financial disclosures. The consultation paper on the proposed amendments to the Listing Requirements is available here. Interested parties are invited to submit their comments and feedback to Bursa Malaysia by 18 May 2022. Read more
  • European Supervisory Authorities clarify application of SFDR for investors: EU’s ESAs have issued a revised statement to promote the consistent application of SFDR. The supervisory bodies clarify disclosure expectations under the Taxonomy Regulation and encourage the application of relevant measures under SFDR. Read more
  • EFRAG issues its Due Process Procedures for Sustainability Reporting Standard Setting: EFRAG announced the publication of the Due Process Procedures for Sustainability Reporting Standard-Setting (the DPP). The provisions contained in the DPP apply to the preparation of draft EU sustainability reporting standards by EFRAG.  


23 Mar, 2022

New Frontiers: Arabesque Launches Research Centre in Singapore


Arabesque recently created an AI and data engineering centre based in Singapore. The centre is supported under the Financial Sector Technology & Innovation – Artificial Intelligence & Data Analytics (FSTI – AIDA) scheme, which aims to strengthen the AIDA ecosystem in the Singapore financial sector. The FSTI – AIDA scheme is funded by the Financial Sector Development Fund, administered by the Monetary Authority of Singapore. Arabesque team members work in close collaboration with the wider Arabesque team, primarily in London, but also in Tokyo, New Delhi, Frankfurt and Boston. 

A key differentiator of the centre is its engineering-first approach. This has a number of benefits; for example: we can quickly experiment and iterate our ideas; our work can be tested at-scale; and we can move our code to production quickly. We continue to maintain our strong engineering partnership with GCP, enabling us to remain at the cutting-edge. See a recent example of our outreach with Google on March 11th here

Team members based in Singapore support the production of our key deliverable, AutoCIO. AutoCIO is an easy-to-use platform that builds customisable investment strategies. For example, by using AutoCIO, investment strategies can be customised to target certain CO2 emissions or certain gender diversity levels. The platform is utilised by Arabesque’s clients- such as DWS, one of the world’s leading asset managers, and BIMB Investment, a Shariah-ESG investment management company in Malaysia.  

Our engineering centre works on specific KPIs as indicated in our press release, and broadly these cover: 

  1. Implementing NLP data to our financial Knowledge Graph; 
  2. An AI Financial Analyst, creating new approaches for financial modelling and analysis; and 
  3. Understanding data bias with application-to-transfer learning 

1. Implementing NLP data to our financial Knowledge Graph 

Problem: The growth of structured data (like quarterly revenues or sales) and unstructured data (like news or social-media) is problematic if we do not have the appropriate tools to meaningfully organise the data. Such diverse sources of data (and particularly unstructured data) are often rich in interconnectedness despite their apparent heterogeneity. Such interconnectedness is often obscured in traditional database structures, thereby limiting the value and AI-based insights that these datasets may otherwise provide. 
In order to address the problem statement above, the project has three key areas: 

  1. The Knowledge Graph as a means to organise interconnected data. 
  2. The tools to traverse the graph, extracting relevant data and study the correlations of entities in the Knowledge Graph. 
  3. The Knowledge Graph being used to train new AI predictive models.

2. An AI Financial Analyst, creating new approaches for financial modelling and analysis 

Problem: Financial analysts typically devote a substantial amount of their time pouring over financial statements, industry reports etc. in order to build complex models for the purpose of corporate valuation, or the prediction of company financials. Aside from the substantial cost of human resource, the absence of modern data-driven AI approaches can ultimately impact the accuracy and integrity of the financial analyst’s output.  
Our approach implements a general machine learning approach that can enable a more  
accurate and holistic approach to the work typically carried out by a financial analyst targeting the following areas: 

  1. DCF (discounted cash flow) predictions, including company/project valuation. 
  2. The inclusion of ESG data within DCF approaches.  
  3. Company financials predictions, e.g. quarterly sales, including fraud detection.  
  4. Event analyses, e.g. Merger likelihoods; Post-IPO stock price analyses; Supply chain impact analyses.  

3. Understanding data bias with application to transfer learning 

Problem: High-quality, labelled datasets are difficult to obtain or produce because of the large amount of time and effort required to label such datasets. Furthermore, these datasets are often subject to various forms of data bias, thereby hindering trust with a machine learning model using such data. 

In order to address the problem statement above, this project has three key areas: 

1. Data sourcing: To deliver datasets of high quality and quantity suitable for financial machine learning. 
2. Data biases: To develop automated tools and systems for capturing and identifying bias in datasets. 
3. Transfer learning: To make advances in the area of transfer learning.  In particular, applying the knowledge obtained from one market to other markets for applications such as making investment predictions. 

The current focus of the centre is a reimaging of our data processing pipelines to maximise our ability to onboard and process our big data sets. Our current job roles reflect this, and we aim to add additional roles soon. We look forward to continuing to expand our team and leading this initiative to provide innovative financial research in the APAC region.  

17 Feb, 2022

Investors seek raw ESG data to power up


This article was originally published by International Financing Review (Investors seek raw ESG data to power up by Tessa Walsh)
ESG is moving into a new phase focused on delivering on net zero commitments that will reshape the provision and collection of ESG data, according to Daniel Klier, president of ESG data investment research and asset manager Arabesque.

After last year’s UN COP26 meeting, investors are moving away from single indicators, such as ESG scores or ratings, and are looking for forward-looking data and flexible raw data feeds to power their own work on investment decisions, risk management and modelling and to meet growing regulatory requirements.

“If you want to move ESG out of the ethical corner into the core of the investment process, you need to put your data at the core of the investment process,” Klier said.

“Investors want to use ESG data to address the use case that they have, rather than be told their ESG score by a data provider.”

Klier was formerly HSBC’s global head of sustainable finance and joined Arabesque last June. Arabesque provides technology for sustainable finance and offers ESG investment strategies, data and insights for financial decision-making using artificial intelligence technology.

The firm’s asset management arm uses mathematical models to target ESG investments and Klier is also CEO of its S-Ray arm, which provides data and ESG metrics to assess the sustainability performance of companies.

Private markets push

Reported data on large, listed companies are backward looking and often inaccurate, and the push of ESG into private markets where data are harder to acquire is requiring a new approach to fill large data gaps.

Arabesque launched ESG Book in December, which is a central digital hub for corporate sustainable information that makes raw data available for free and charges for any analytics created. 

Investors and banks can also invite companies to disclose straight onto ESG Book’s platform to gather necessary data. It has been created with a group of founding partners that includes the IFC, Deutsche Bank and HSBC and lenders can incorporate data disclosure into credit agreements.

“The interesting discussion of the moment is how do you get into private markets and how do you use alternative data sources to turn this into insight,” Klier said.

“People want raw data. There’s so much wealth in unstructured data but people need help to create investment sights that help capital allocation.”

Arabesque has recently announced a partnership that integrates its ESG data products with cloud technology company Snowflake, which will allow clients to import ESG data products into their technology.

This will give access to Arabesque’s full data set in real time, which includes sustainability performance metrics and green revenue data, alignment with Taskforce on Climate-Related Financial Disclosures and Sustainable Finance Disclosure Regulation as well as temperature scores and UN Global Compact scores.

These new data sets are expected to be used to develop new ESG funds that target more specific ESG topics with investment propositions such as climate funds and energy transition funds that will help managers to counter accusations of greenwashing.

3 Feb, 2022

TCFD Alignment Barometer: Measuring Climate Disclosure


The Task Force on Climate Related Financial Disclosures (TCFD) was established in 2015 by the Financial Stability Board to develop recommendations for more effective climate related disclosures. In 2017, the TCFD published a set of recommendations to guide companies in providing better climate related reporting, which has since become the global standard for climate disclosures. The TCFD Alignment Barometer, delivered through ESG Book, supports corporates and investors in understanding the TCFD recommendations and the reporting landscape.

To read the full article, click here.